Keywords

Abstract

Humans and primates are remarkably good at pattern recognition
and outperform the best machine vision systems with respect
to almost any measure. Building a computational model that
emulates the architecture and information processing in biological
neural systems has always been an attractive target. To build a
computational model that closely follows the information processing
and architecture of the visual cortex, in this paper, we have improved
the latency-phase encoding to express the external stimuli in a
more abstract manner. Moreover, inspired by recent ﬁndings in
the biological neural system, including architecture, encoding, and
learning theories, we have proposed a feedforward computational
model of spiking neurons that emulates object recognition of the
visual cortex for pattern recognition. Simulation results showed that
the proposed computational model can perform pattern recognition
task well. In addition, the success of this computational model
suggests a plausible proof for feedforward architecture of pattern
recognition in the visual cortex.